Recommendations systems are becoming increasingly common in online sales and digital media sites. Such systems often use recommendation algorithms and information about a user's preferences and purchasing behavior to recommend content and/or goods (which can collectively be referred to as items) for which the user may be interested. Such recommendation algorithms may, for example, be developed by data scientists, and then handed over the software engineers for final coding before such algorithms are ready for providing actual recommendations to actual clients. A potential problem with this scheme is that the software engineers may not fully understand the logic behind the algorithms, and thus, may inadvertently change their functions. Another potential problem with recommendation systems is that they may provide stale recommendations, meaning they may recommend items that are no longer available (e.g., the items may no longer be in inventory), and/or they may not consider new inventory (e.g., if a recommendation algorithm was run before an item or inventory database was updated). Additionally, recommendation systems, depending upon their design, may take an inordinate amount of time to provide recommendations, which may frustrate clients, potentially causing companies to lose potential sales.